Biology subjects

Rizvi, S. A.

Publications and source records attributed to Rizvi, S. A..

5 recordsLinked to original sources

bioRxiv2025

LiaS-dependent activation of the MadR regulon enables cross-talk between Enterococcus faecalis cell envelope defense systems

Enterococci are gastrointestinal commensals that must defend their cell envelope against antimicrobial peptides derived from the host and other members of the microbiota. The signaling systems LiaFSR and MadRS are pivotal for survival in the presence of antimicrobial peptides and antimicrobial peptide-like antibiotics such as daptomycin. Both systems possess a signaling histidine kinase (LiaS, MadS) and cognate response regulator (LiaR, MadR) that activate transcription of distinct sets of effector genes. Using isogenic deletion strains, we noted differences in daptomycin minimum inhibitory concentration (MIC) between the laboratory strain E. faecalis OG1RF (1.5 {micro}g/mL), OG1RF{Delta}madR (0.38 {micro}g/mL), and OG1RF{Delta}madS (4 {micro}g/mL). Transcriptional analysis of the MadR regulon showed a daptomycin-dependent increase in madG, madL, and dltA gene expression in the OG1RF{Delta}madS background, suggesting activation of the LiaFSR system may provide a cross-regulatory role. Deletion of the liaS gene in OG117{Delta}madS was associated with a significant decrease in daptomycin MIC and loss of madG expression, one of the most differentially expressed genes on activation of the MadR regulon. Using microscale thermophoresis, LiaS showed a similar binding affinity to both LiaR (Kd 2.42 {micro}M) and MadR (Kd 5.02 {micro}M), while MadS showed higher affinity for its cognate regulator MadR (Kd 8.08 {micro}M) than for LiaR (Kd 25.7 {micro}M). Taken together, our findings indicate that the MadR regulon can be expressed independent of MadS-induced signaling, likely through cross-talk between LiaS and MadR. Thus, enterococci have evolved an interconnected network of cell envelope signaling that permits bacterial survival in the presence of antibiotics and antimicrobial peptides.

microbiology↗

bioRxiv2025

Scaling Large Language Models for Next-Generation Single-Cell Analysis

AO_SCPLOWBSTRACTC_SCPLOWSingle-cell RNA sequencing has transformed our understanding of cellular diversity, yet current single-cell foundation models (scFMs) remain limited in their scalability, flexibility across diverse tasks, and ability to natively integrate textual information. In this work, we build upon the Cell2Sentence (C2S) framework, which represents scRNA-seq profiles as textual "cell sentences," to train Large Language Models (LLMs) on a corpus comprising over one billion tokens of transcriptomic data, biological text, and metadata. Scaling the model to 27 billion parameters yields consistent improvements in predictive and generative capabilities and supports advanced downstream tasks that require synthesis of information across multi-cellular contexts. Targeted fine-tuning with modern reinforcement learning techniques produces strong performance in perturbation response prediction, natural language interpretation, and complex biological reasoning. This predictive strength enabled a dual-context virtual screen that nominated the kinase inhibitor silmitasertib (CX-4945) as a candidate for context-selective upregulation of antigen presentation. Experimental assessment in human cell models unseen during training supported this prediction, demonstrating that C2S-Scale can effectively guide the discovery of context-conditioned biology. C2S-Scale unifies transcriptomic and textual data at unprecedented scales, surpassing both specialized single-cell models and general-purpose LLMs to provide a platform for next-generation single-cell analysis and the development of "virtual cells."

bioinformatics↗

bioRxiv2024

Cefiderocol heteroresistance associated with mutations in TonB-dependent receptor genes in Pseudomonas aeruginosa of clinical origin

The siderophore-cephalosporin cefiderocol(FDC) presents a promising treatment option for carbapenem-resistant (CR) P. aeruginosa (PA). FDC circumvents traditional porin and efflux mediated resistance by utilizing TonB-dependent receptors (TBDRs) to access the periplasmic space. Emerging FDC resistance has been associated with loss of function mutations within TBDR genes or the regulatory genes controlling TBDR expression. Further, difficulties with antimicrobial susceptibility testing (AST) and unexpected negative clinical treatment outcomes have prompted concerns for heteroresistance, where a single lineage isolate contains resistant subpopulations not detectable by standard AST. This study aimed to evaluate the prevalence of TBDR mutations among clinical isolates of P. aeruginosa and the phenotypic effect on FDC susceptibility and heteroresistance. We evaluated the sequence of pirR, pirS, pirA, piuA or piuD from 498 unique isolates collected before the introduction of FDC from 4 clinical sites in Portland, OR (1), Houston, TX (2), and Santiago, Chile (1). At some clinical sites, TBDR mutations were seen in up to 25% of isolates, and insertion, deletion, or frameshift mutations were predicted to impair protein function were seen in 3% of all isolates (n=15). Using population analysis profile testing, we found that P. aeruginosa with major TBDR mutations were enriched for a heteroresistant phenotype and undergo a shift in the susceptibility distribution of the population as compared to susceptible strains with wild type TBDR genes. Our results indicate that mutations in TBDR genes predate the clinical introduction of FDC, and these mutations may predispose to the emergence of FDC resistance.

microbiology↗

bioRxiv2023

Cell2Sentence: Teaching Large Language Models the Language of Biology

We introduce Cell2Sentence (C2S), a novel method to directly adapt large language models to a biological context, specifically single-cell transcriptomics. By transforming gene expression data into "cell sentences," C2S bridges the gap between natural language processing and biology. We demonstrate cell sentences enable the fine-tuning of language models for diverse tasks in biology, including cell generation, complex cell-type annotation, and direct data-driven text generation. Our experiments reveal that GPT-2, when fine-tuned with C2S, can generate biologically valid cells based on cell type inputs, and accurately predict cell types from cell sentences. This illustrates that language models, through C2S fine-tuning, can acquire a significant understanding of single-cell biology while maintaining robust text generation capabilities. C2S offers a flexible, accessible framework to integrate natural language processing with transcriptomics, utilizing existing models and libraries for a wide range of biological applications.

bioinformatics↗

bioRxiv2023

BrainLM: A foundation model for brain activity recordings

AO_SCPLOWBSTRACTC_SCPLOWWe introduce the Brain Language Model (BrainLM), a foundation model for brain activity dynamics trained on 6,700 hours of fMRI recordings. Utilizing self-supervised masked-prediction training, BrainLM demonstrates proficiency in both fine-tuning and zero-shot inference tasks. Fine-tuning allows for the accurate prediction of clinical variables like age, anxiety, and PTSD as well as forecasting of future brain states. Critically, the model generalizes well to entirely new external cohorts not seen during training. In zero-shot inference mode, BrainLM can identify intrinsic functional networks directly from raw fMRI data without any network-based supervision during training. The model also generates interpretable latent representations that reveal relationships between brain activity patterns and cognitive states. Overall, BrainLM offers a versatile and interpretable framework for elucidating the complex spatiotemporal dynamics of human brain activity. It serves as a powerful "lens" through which massive repositories of fMRI data can be analyzed in new ways, enabling more effective interpretation and utilization at scale. The work demonstrates the potential of foundation models to advance computational neuroscience research.

neuroscience↗